MICROPLASTICS IN AQUATIC ECOSYSTEMS: PATHWAYS, IMPACTS AND INTEGRATED SOLUTIONS FOR ENVIRONMENT AND HUMAN HEALTH
Bibliographic record
Abstract
Plastic pollution has rapidly evolved into a global environmental crisis, with microplastics emerging as ubiquitous and persistent contaminants across freshwater and marine ecosystems. This review synthesizes current knowledge on the origin, distribution, and ecological consequences of microplastics, emphasizing their complex environmental behavior and widespread biological uptake. Microplastics are introduced through diverse pathways, including wastewater effluents, urban and agricultural runoff, atmospheric deposition, and the degradation of larger plastic debris. Once in the aquatic environment, they undergo transformation via photochemical, mechanical, and biological processes, facilitating their dispersal and interaction with biota and co-pollutants. Ingestion of microplastics by a broad range of organisms has been documented, with evidence of bioaccumulation, trophic transfer, and physiological harm. Moreover, microplastics act as vectors for hazardous chemicals and pathogens, raising critical concerns for food safety and human health. Current removal technologies are limited in scope and efficiency, particularly in natural settings. We highlight an urgent need for integrated solutions that combine upstream interventions (e.g., reduced plastic use and improved product design) with downstream innovations (e.g., advanced filtration, bioremediation, and policy enforcement). A global, interdisciplinary response is essential to mitigate the long-term impacts of microplastic pollution and safeguard aquatic ecosystems and public health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".